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Human activity recognition using wearable devices

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thesis
posted on 28.03.2022, 11:05 by Jianchao Lu
Human activity recognition (HAR) is a key application on wearable devices in the areas of fitness tracking, healthcare and elder care support. However, inaccurate recognition results may cause an adverse effect on users or even an unpredictable accident. Therefore, it is necessary to improve the accuracy of human activity recognition.This thesis aims to provide effective and efficient HAR methods to address main challenges of HAR, which can be divided into the following three contributions. The first contribution is a novel feature extraction and selection algorithm that addresses the interclass similarity problem in the confounding activity recognition. The second contribution is a novel approach of leveraging local and global features, which addresses both the intraclass variability and interclass similarity problems in HAR. The third contribution is a multiscale feature engineering approach, which leverages local and global features and addresses the negative effect on HAR caused by users' different habits. For the proposed approaches, extensive experiments have been conducted on real datasets or real scenarios. The experiments have demonstrated the proposed methods are superior to the state of the art.

History

Table of Contents

1. Introduction -- 2. Literature review -- 3. Novel feature extraction and selection algorithm for confounding activity recognition -- 4. Leveraging local and global features to detect human daily activities -- 5. MFE-HAR : multiscale feature engineering for human activity recognition using wearable sensors -- 6. Conclusion -- Bibliography.

Notes

Bibliography: pages 52-56 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

MRes, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award

2019

Principal Supervisor

Xi Zheng

Rights

Copyright Jianchao Lu 2019. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (x, 56 pages) colour illustrations

Former Identifiers

mq:71577 http://hdl.handle.net/1959.14/1275810